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Found 7 papers, 6 papers with code
A Cautionary Tale: On the Role of Reference Data in Empirical Privacy Defenses
Within the realm of privacy-preserving machine learning, empirical privacy defenses have been proposed as a solution to achieve satisfactory levels of training data privacy without a significant drop in model utility.
Federated Learning under Heterogeneous and Correlated Client Availability
To this purpose, CA-Fed dynamically adapts the weight given to each client and may ignore clients with low availability and large correlation.
Federated Learning for Data Streams
Federated learning (FL) is an effective solution to train machine learning models on the increasing amount of data generated by IoT devices and smartphones while keeping such data localized.
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
In this work, we propose a novel cross-silo dataset suite focused on healthcare, FLamby (Federated Learning AMple Benchmark of Your cross-silo strategies), to bridge the gap between theory and practice of cross-silo FL.
Personalized Federated Learning through Local Memorization
Federated learning allows clients to collaboratively learn statistical models while keeping their data local.
Federated Multi-Task Learning under a Mixture of Distributions
The increasing size of data generated by smartphones and IoT devices motivated the development of Federated Learning (FL), a framework for on-device collaborative training of machine learning models.
Throughput-Optimal Topology Design for Cross-Silo Federated Learning
Federated learning usually employs a client-server architecture where an orchestrator iteratively aggregates model updates from remote clients and pushes them back a refined model.
